1
Faculty of Art, Computing and Creative Industries, Universiti Pendidikan Sultan Idris, Malaysia
(raghad.alfaisal81@gmail.com)
2
Faculty of Engineering and IT, The British University in Dubai, Dubai, UAE
(............)
3
Department of Languages and Translation, Arar Community College, KSA
(mrs.aseel@gmail.com)
4
Department of Languages and Translation, Arar Community College, KSA
(sakurarose31@gmail.com)
5
Faculty of Arts, Department of English Language and Literature, Yarmouk University, Irbid, Jordan
(dodo.44844@gmail.com)
Abstract :
The outbreak of COVID-19 led to the foundation of modern techniques of learning which involves metaverse. Specifically in the medical field, where cross-border medical training became out of question. Opportunities for medical students to practice were greatly reduced as there was very less physical interaction with patients due to the COVID-19 pandemic. However, metaverse proved to be of great help for medical staff to gain education virtually who came to the UAE to acquire proficient skills related to medical technology. New digital approaches based on metaverse technology are evolving in the UAE medical groups to address restrictions arising by using current teleconferencing platforms like Zoom in providing effective medical training. The goal of this research is to find out the effect of using the metaverse system for medical training in the UAE and students’ perceptions of it. The adoption features of trialability, observability, perceived pleasure, perceived ubiquity, perceived worth, personal innovativeness, and Technology Acceptance Model (TAM) components are all included in the conceptual model. The study’s novelty comes from its conceptual model, which links both personal and technology-based elements. Furthermore, the present work will employ a novel approach of hybrid analysis to perform machine-learning (ML) based structural equation modeling analysis (SEM). In addition, this study is assisted by importance-performance map analysis (IPMA) to evaluate the performance and importance of presumed factors. As this work is one of the rare attempts to utilize machine learning algorithms in predicting the intention to use metaverse systems, the methodological aspect of the study is also of great use. The adoption of a complementary multi-analytical approach is thought to provide a novel contribution to the information systems (IS) field. This study is also significant in assisting medical authorities to judge the importance of each factor and guiding them to opt for relevant strategies and techniques depending on the significance of the factors.
Keywords :
COVID-19; Technology Acceptance Model; PLS-SEM; and Machine Learning Models.
References :
[1] C. Collins, “Looking to the future: Higher education in the Metaverse,” Educ. Rev., vol. 43, no. 5, pp. 51–63, 2008.
[2] K. MacCallum and D. Parsons, “Teacher perspectives on mobile augmented reality: The potential of metaverse for learning,” in World Conference on Mobile and Contextual Learning, 2019, pp. 21–28.
[3] N. Stephenson, “Snowcrash. London: ROC.” Penguin, 1992.
[4] J. Díaz, C. Saldaña, and C. Avila, “Virtual World as a Resource for Hybrid Education,” Int. J. Emerg. Technol. Learn., vol. 15, no. 15, pp. 94–109, 2020.
[5] J. B. P. Arcila, “Metaversos Para el máster iberoamericano en educación en entornos virtuales,” Etic@ net. Rev. científica electrónica Educ. y Comun. en la Soc. del Conoc., vol. 14, no. 2, pp. 227–248, 2014.
[6] I. V Márquez, “Metaversos y educación: Second Life como plataforma educativa,” Rev. ICONO14 Rev. científica Comun. y Tecnol. emergentes, vol. 9, no. 2, pp. 151–166, 2011.
[7] S. Farjami, R. Taguchi, K. T. Nakahira, Y. Fukumura, and H. Kanematsu, “W-02 Problem Based Learning for Materials Science Education in Metaverse,” in JSEE Annual Conference International Session Proceedings 2011 JSEE Annual Conference, 2011, pp. 20–23.
[8] H. Kanematsu, T. Kobayashi, N. Ogawa, D. M. Barry, Y. Fukumura, and H. Nagai, “Eco car project for Japan students as a virtual PBL class,” Procedia Comput. Sci., vol. 22, pp. 828–835, 2013.
[9] H. Kanematsu, T. Kobayashi, N. Ogawa, Y. Fukumura, D. M. Barry, and H. Nagai, “Nuclear energy safety project in metaverse,” in Intelligent Interactive Multimedia: Systems and Services, Springer, 2012, pp. 411–418.
[10] D. M. Barry et al., “International comparison for problem based learning in metaverse,” ICEE ICEER, vol. 6066, 2009.
[11] J. Jeon and S. K. Jung, “Exploring the educational applicability of Metaverse-based platforms,” 한국정보교육학회: 학술대회논문집, pp. 361–368, 2021.
[12] H.-C. Han, “From Visual Culture in the Immersive Metaverse to Visual Cognition in Education,” in Cognitive and Affective Perspectives on Immersive Technology in Education, IGI Global, 2020, pp. 67–84.
[13] F. D. Davis, “Perceived usefulness, perceived ease of use, and user acceptance of information technology,” MIS Q., pp. 319–340, 1989.
[14] F. D. Davis, “A technology acceptance model for empirically testing new end-user information systems: Theory and results.” Massachusetts Institute of Technology, 1985.
[15] M. Kleijnen, K. De Ruyter, and M. Wetzels, “An assessment of value creation in mobile service delivery and the moderating role of time consciousness,” J. Retail., vol. 83, no. 1, pp. 33–46, 2007.
[16] S. Okazaki, H. Li, and M. Hirose, “A Study of Mobile Advertising in Japan,” J. Advert., vol. 38, no. 4, pp. 63–77, 2009.
[17] S. Okazaki and F. Mendez, “Perceived ubiquity in mobile services,” J. Interact. Mark., vol. 27, no. 2, pp. 98–111, 2013.
[18] A. R. Ashraf, N. Thongpapanl, B. Menguc, and G. Northey, “The role of m-commerce readiness in emerging and developed markets,” J. Int. Mark., vol. 25, no. 2, pp. 25–51, 2017.
[19] I. K. Mensah and D. S. Mwakapesa, “The Impact of Context Awareness and Ubiquity on Mobile Government Service Adoption,” Mob. Inf. Syst., vol. 2022, 2022.
[20] C. M. Ringle, S. Wende, and J.-M. Becker, “SmartPLS 3. Bönningstedt: SmartPLS.” 2015.
[21] I. A. Akour, R. S. Al-Maroof, R. Alfaisal, and S. A. Salloum, “A conceptual framework for determining metaverse adoption in higher institutions of gulf area: An empirical study using hybrid SEM-ANN approach,” Comput. Educ. Artif. Intell., p. 100052, 2022.
[22] R. S. Al-Maroof et al., “Acceptance determinants of 5G services,” Int. J. Data Netw. Sci., vol. 5, no. 4, pp. 613–628, 2021.
[23] R. Al-Maroof et al., “The acceptance of social media video for knowledge acquisition, sharing and application: A com-parative study among YouTube users and TikTok Users’ for medical purposes,” Int. J. Data Netw. Sci., vol. 5, no. 3, pp. 197–214, 2021.
[24] R. S. Al-Maroof et al., “The effectiveness of online platforms after the pandemic: will face-to-face classes affect students’ perception of their Behavioural Intention (BIU) to use online platforms?,” in Informatics, 2021, vol. 8, no. 4, p. 83.
[25] M. Elareshi, M. Habes, E. Youssef, S. A. Salloum, R. Alfaisal, and A. Ziani, “SEM-ANN-based approach to understanding students’ academic-performance adoption of YouTube for learning during Covid,” Heliyon, p. e09236, 2022.
[26] K. Alhumaid et al., “Predicting the Intention to Use Audi and Video Teaching Styles: An Empirical Study with PLS-SEM and Machine Learning Models,” in International Conference on Advanced Machine Learning Technologies and Applications, 2022, pp. 250–264.
[27] R. S. Al-Maroof et al., “Students’ perception towards behavioral intention of audio and video teaching styles: An acceptance study,” Int. J. Data Netw. Sci., vol. 6, no. 2, p. 603, 2022.
[28] R. Alfaisal et al., “PREDICTING THE INTENTION TO USE GOOGLE GLASS IN THE EDUCATIONAL PROJECTS: A HYBRID SEM-ML APPROACH.”
[29] I. Akour, N. Alnazzawi, R. Alfaisal, and S. A. Salloum, “USING CLASSICAL MACHINE LEARNING FOR PHISHING WEBSITES DETECTION FROM URLS.”
[30] J. Hair, C. L. Hollingsworth, A. B. Randolph, and A. Y. L. Chong, “An updated and expanded assessment of PLS-SEM in information systems research,” Ind. Manag. Data Syst., vol. 117, no. 3, pp. 442–458, 2017.
[31] N. Urbach and F. Ahlemann, “Structural equation modeling in information systems research using partial least squares,” J. Inf. Technol. theory Appl., vol. 11, no. 2, pp. 5–40, 2010.
[32] J. F. Hair Jr, G. T. M. Hult, C. Ringle, and M. Sarstedt, A primer on partial least squares structural equation modeling (PLS-SEM). Sage Publications, 2016.
[33] D. L. Goodhue, W. Lewis, and R. Thompson, “Does PLS have adavantages for small sample size or non-normal data?,” MIS Quaterly, 2012.
[34] D. Barclay, C. Higgins, and R. Thompson, The Partial Least Squares (pls) Approach to Casual Modeling: Personal Computer Adoption Ans Use as an Illustration. 1995.
[35] C. Fornell and D. F. Larcker, “Evaluating Structural Equation Models With Unobservable Variables and Measurement Error,” J. Mark. Res., vol. 18, no. 1, pp. 39–50, 1981.
[36] J. F. Hair, W. C. Black Jr, B. J. Babin, and R. E. Anderson, “Multivariate Data Analysis”, Pearson Prentice Hall, USA,” 2010.
[37] J. Henseler, C. M. Ringle, and M. Sarstedt, “A new criterion for assessing discriminant validity in variance-based structural equation modeling,” J. Acad. Mark. Sci., vol. 43, no. 1, pp. 115–135, 2015.
[38] S. F. S. Alhashmi, S. A. Salloum, and C. Mhamdi, “Implementing Artificial Intelligence in the United Arab Emirates Healthcare Sector: An Extended Technology Acceptance Model,” Int. J. Inf. Technol. Lang. Stud., vol. 3, no. 3, 2019.
[39] S. A. Salloum and K. Shaalan, “Adoption of E-Book for University Students,” in International Conference on Advanced Intelligent Systems and Informatics, 2018, pp. 481–494.
[40] M. T. Alshurideh, B. Al Kurdi, and S. A. Salloum, “The moderation effect of gender on accepting electronic payment technology: a study on United Arab Emirates consumers,” Rev. Int. Bus. Strateg., 2021.
[41] A. Aburayya et al., “An Empirical Examination of the Effect of TQM Practices on Hospital Service Quality: An Assessment Study in UAE Hospitals.”
[42] R. Saeed Al-Maroof, K. Alhumaid, and S. Salloum, “The Continuous Intention to Use E-Learning, from Two Different Perspectives,” Educ. Sci., vol. 11, no. 1, p. 6, 2020.
[43] M. Alghizzawi, M. Habes, and S. A. Salloum, The Relationship Between Digital Media and Marketing Medical Tourism Destinations in Jordan: Facebook Perspective, vol. 1058. 2020.
[44] R. S. Al-Maroof, S. A. Salloum, A. Q. AlHamadand, and K. Shaalan, “Understanding an Extension Technology Acceptance Model of Google Translation: A Multi-Cultural Study in United Arab Emirates,” Int. J. Interact. Mob. Technol., vol. 14, no. 03, pp. 157–178, 2020.
[45] M. Al-Emran, I. Arpaci, and S. A. Salloum, “An empirical examination of continuous intention to use m-learning: An integrated model,” Educ. Inf. Technol., pp. 1–20, 2020.
[46] S. A. Salloum, A. Q. M. Alhamad, M. Al-Emran, A. A. Monem, and K. Shaalan, “Exploring Students’ Acceptance of E-Learning Through the Development of a Comprehensive Technology Acceptance Model,” IEEE Access, vol. 7, pp. 128445–128462, 2019.
[47] W. W. Chin, “The partial least squares approach to structural equation modeling,” Mod. methods Bus. Res., vol. 295, no. 2, pp. 295–336, 1998.
[48] I. Arpaci, “A hybrid modeling approach for predicting the educational use of mobile cloud computing services in higher education,” Comput. Human Behav., vol. 90, pp. 181–187, 2019.
[49] H. Yousuf, A. Q. Al-Hamad, and S. Salloum, “An Overview on CryptDb and Word2vec Approaches.”
[50] A. Wahdan, S. A. Salloum, and K. Shaalan, “Qualitative study in Natural Language Processing: text classification,” in International Conference on Emerging Technologies and Intelligent Systems, 2021, pp. 83–92.
[51] F. Almatrooshi, S. Alhammadi, S. A. Salloum, and K. Shaalan, “Text and web content mining: a systematic review,” in International Conference on Emerging Technologies and Intelligent Systems, 2021, pp. 79–87.
[52] S. Salloum, T. Gaber, S. Vadera, and K. Shaalan, “Phishing Website Detection from URLs Using Classical Machine Learning ANN Model,” in International Conference on Security and Privacy in Communication Systems, 2021, pp. 509–523.
[53] A. A. Khamees, H. D. Hejazi, M. Alshurideh, and S. A. Salloum, “Classifying Audio Music Genres Using CNN and RNN,” Adv. Mach. Learn. Technol. Appl. Proc. AMLTA 2021, p. 315.
[54] A. Wahdan, S. A. Salloum, and K. Shaalan, “Text Classification of Arabic Text: Deep Learning in ANLP,” in Advanced Machine Learning Technologies and Applications: Proceedings of AMLTA 2021, 2021, pp. 95–103.
[55] S. A. Salloum, “Classifying Audio Music Genres Using a Multilayer Sequential Model,” Adv. Mach. Learn. Technol. Appl. Proc. AMLTA 2021, p. 301.
[56] E. Frank et al., “Weka-a machine learning workbench for data mining,” in Data mining and knowledge discovery handbook, Springer, 2009, pp. 1269–1277.
[57] K. M. Alomari, A. Q. AlHamad, and S. Salloum, “Prediction of the Digital Game Rating Systems based on the ESRB.”
[58] S. A. Salloum, “Sentiment Analysis in Dialectal Arabic: A Systematic Review,” Adv. Mach. Learn. Technol. Appl. Proc. AMLTA 2021, p. 407.
[59] H. Yousuf, M. Lahzi, S. A. Salloum, and K. Shaalan, “A systematic review on sequence-to-sequence learning with neural network and its models.,” Int. J. Electr. Comput. Eng., vol. 11, no. 3, 2021.
[60] H. Yousuf and S. Salloum, “Survey Analysis: Enhancing the Security of Vectorization by Using word2vec and CryptDB.”
[61] A. Alshamsi, R. Bayari, and S. Salloum, “Sentiment analysis in English Texts,” Adv. Sci. Technol. Eng. Syst., vol. 5, no. 6, 2020.
[62] S. A. Salloum, M. Al-Emran, and K. Shaalan, “Mining Text in News Channels: A Case Study from Facebook,” Int. J. Inf. Technol. Lang. Stud., vol. 1, no. 1, pp. 1–9, 2017.
[63] S. A. Salloum, R. Khan, and K. Shaalan, “A Survey of Semantic Analysis Approaches,” in Joint European-US Workshop on Applications of Invariance in Computer Vision, 2020, pp. 61–70.
[64] C. Mhamdi, M. Al-Emran, and S. A. Salloum, Text mining and analytics: A case study from news channels posts on Facebook, vol. 740. 2018.
[65] S. A. Salloum, M. Al-Emran, A. A. Monem, and K. Shaalan, Using text mining techniques for extracting information from research articles, vol. 740. 2018.
[66] I. Akour, M. Alshurideh, B. Al Kurdi, A. Al Ali, and S. Salloum, “Using machine learning algorithms to predict people’s intention to use mobile learning platforms during the COVID-19 pandemic: Machine learning approach,” JMIR Med. Educ., vol. 7, no. 1, 2021.
| Style | # |
|---|---|
| MLA | Raghad M. Alfaisal, Aisha Zare, Aseel M. Alfaisal, Rose Aljanada, Ghadeer Wael Abukhalil. "The acceptance of metaverse system: a hybrid SEM-ML approach." International Journal of Advances in Applied Computational Intelligence, Vol. 1, No. 1, 2022 ,PP. 34-44 (Doi : https://doi.org/10.54216/IJAACI.010103) |
| APA | Raghad M. Alfaisal, Aisha Zare, Aseel M. Alfaisal, Rose Aljanada, Ghadeer Wael Abukhalil. (2022). The acceptance of metaverse system: a hybrid SEM-ML approach. Journal of International Journal of Advances in Applied Computational Intelligence, 1 ( 1 ), 34-44 (Doi : https://doi.org/10.54216/IJAACI.010103) |
| Chicago | Raghad M. Alfaisal, Aisha Zare, Aseel M. Alfaisal, Rose Aljanada, Ghadeer Wael Abukhalil. "The acceptance of metaverse system: a hybrid SEM-ML approach." Journal of International Journal of Advances in Applied Computational Intelligence, 1 no. 1 (2022): 34-44 (Doi : https://doi.org/10.54216/IJAACI.010103) |
| Harvard | Raghad M. Alfaisal, Aisha Zare, Aseel M. Alfaisal, Rose Aljanada, Ghadeer Wael Abukhalil. (2022). The acceptance of metaverse system: a hybrid SEM-ML approach. Journal of International Journal of Advances in Applied Computational Intelligence, 1 ( 1 ), 34-44 (Doi : https://doi.org/10.54216/IJAACI.010103) |
| Vancouver | Raghad M. Alfaisal, Aisha Zare, Aseel M. Alfaisal, Rose Aljanada, Ghadeer Wael Abukhalil. The acceptance of metaverse system: a hybrid SEM-ML approach. Journal of International Journal of Advances in Applied Computational Intelligence, (2022); 1 ( 1 ): 34-44 (Doi : https://doi.org/10.54216/IJAACI.010103) |
| IEEE | Raghad M. Alfaisal, Aisha Zare, Aseel M. Alfaisal, Rose Aljanada, Ghadeer Wael Abukhalil, The acceptance of metaverse system: a hybrid SEM-ML approach, Journal of International Journal of Advances in Applied Computational Intelligence, Vol. 1 , No. 1 , (2022) : 34-44 (Doi : https://doi.org/10.54216/IJAACI.010103) |